AI-102 Implement an agentic solution Practice Question
You are developing an agentic solution that uses Azure AI Search as a knowledge base. The agent needs to retrieve the most relevant documents based on a user query. You notice that the agent sometimes returns irrelevant results. Which configuration should you adjust?
⚠ Common exam trap
Watch out — candidates often confuse the 'top' parameter with relevance scoring or assume that adjusting scoring thresholds (like minimum score) will fix retrieval gaps, when in fact the issue is simply that the initial candidate set is too small.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Increase the 'top' parameter to retrieve more candidate documents
Increasing the 'top' parameter retrieves more candidate documents from Azure AI Search, which gives the agent a larger pool of potentially relevant results to rank and filter. This can help when the initial set of top documents misses relevant content, as the agent can then apply its own reasoning or additional reranking to select the best matches. The 'top' parameter controls the number of search results returned, not the relevance scoring itself.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Change the language analyzer to a different language
Why it's wrong here
Analyzer affects tokenization, not relevance.
- ✓
Increase the 'top' parameter to retrieve more candidate documents
Why this is correct
More candidates increase chance of relevant results.
- ✗
Disable semantic ranking
Why it's wrong here
Semantic ranking improves relevance.
- ✗
Decrease the minimum search score threshold
Why it's wrong here
Lower threshold includes more irrelevant results.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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